Other approaches leverage deep learning to build efficiently parameterized Koopman models with high fidelity. Methods using linear recurrent autoencoders build effective, low-dimensional Koopman models that are not measurement-inclusive [1]}, [2]}, [3]}. To do this, they pass observables from the learned function space, via the decoder, back to the original model's state-space. Other work has, under the assumption of ergodicity, delegated the process of choosing a lifting map for EDMD to deep learning [4]}.
